Field
Embodiments presented herein provide techniques for simplifying models of robots, and, in particular, for automatic task-specific model reduction for robots.
Description of the Related Art
In robot control, simplified dynamics models are often used to represent the robot, as it is difficult to design controllers that control full dynamics models having many degrees of freedom (DOF). Typically, simplified models have fewer DOF than full models and are linearized to apply techniques from linear control theory. Examples of simplified models include the one-joint inverted pendulum model, the two-joint inverted pendulum model, the cart-table model, the inverted pendulum with reaction wheel, the double inverted pendulum, and the linear biped model.
Conventionally, controller developers formulate these simplified models manually based on their intuition. Little work has been done to investigate how well such simplified models match the dynamics of the original, high-dimensional models. Moreover, in addition to the choice of the model, the controller developer must pick two different mappings, one that maps the state of the full model to the reduced state, and another that maps the control inputs of the reduced model to those of the full model. This control input mapping is tricky, as there are infinite possible mappings from a low-dimensional space to a high-dimensional space. Kinetic energy equivalence or angular momentum equivalence are generally used to pick control input mappings.